Bayesian and hybrid Cramer-Rao bounds for QAM dynamical phase estimation
Jianxiao Yang (ENSTA ParisTech U2IS/IS), Benoit Geller (ENSTA, ParisTech U2IS/IS), A Wei (CEDRIC)
TL;DR
This paper derives analytical Bayesian and hybrid Cramer-Rao bounds for QAM signal phase estimation, reducing computational complexity and illustrating their behavior with respect to SNR through simulations.
Contribution
It provides explicit formulas for Bayesian and hybrid CRBs in QAM phase estimation, avoiding matrix inversions and enabling efficient performance analysis.
Findings
Derived analytical expressions for BCRB and HCRB
Reduced computational complexity in bounds calculation
Illustrated bounds behavior with SNR through simulations
Abstract
-In this paper, we study Bayesian and hybrid Cramer-Rao bounds for the dynamical phase estimation of QAM modulated signals. We present the analytical expressions for the various CRBs. This avoids the calculation of any matrix inversion and thus greatly reduces the computation complexity. Through simulations, we also illustrate the behaviors of the BCRB and of the HCRB with the signal-to-noise ratio. Index Terms-Bayesian Cramer-Rao Bound (BCRB), Hybrid Cramer-Rao Bound (HCRB), Synchronization Performance
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